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The Twister Sisters, Peggy Willenberg and Melanie Metz Topic
NEWSLETTER TWIN CITIES CHAPTER AMERICAN METEOROLOGICAL SOCIETY September, 2007 Vol. 29 No. 1 The meeting of the Twin Cities Chapter of the AMS will be at 7 PM September 18th, 2007 Twin Cities WFO, Chanhassen, MN Specific directions to the meeting can be found on page 5. AMS chapter members, interested acquaintances and potential members are invited to attend. Featured Speaker: The Twister Sisters, Peggy Willenberg and Melanie Metz Topic: Chasing the Greensburg, KS, tornado of May 4, 2007 “Ok, so we're not actually sisters... but we are true Twister Sisters! We started chasing together in 2001 and have become best of friends. We both enjoy life, like a good glass of wine, think the same way, and have a common desire... to capture the perfect wedge! We always agree on a target and continue to learn more from each successful chase and each bust. Every chase is a new and exciting challenge.” “We both have a Bachelor's Degree in Science and are completed several Meteorology courses at Saint Cloud State University. We also attended a Severe Storm Forecasting class instructed by Tim Vasquez and attended the College of Du Page Severe Storms Forecasting Conference. During the chase season, we work for FOX 9 News, KMSP in Minneapolis, MN and teach basic and advanced Skywarn classes. We have given educational severe weather talks for two consecutive years at a HAM radio fest in St. Paul, MN; The Importance of Boundaries for Severe Storm Initiation and Tornadic vs. Non-Tornadic Wall Clouds. During the summer of 2002, we volunteered as IHOP Mobile Mesonet Operators at the National Severe Storms Laboratory. -
NEXRAD Product Improvement Program Update
NEXRAD Product Improvement Program Update Greg Cate OS&T/PPD MARCH 2006 1 TOPICS ORDA • Dual Polarization • Super-Resolution – Recommendation to proceed • FAA Radar Data Ingest •CASA 2 ORDA • ORDA subsystem replaces the current WSR-88D Radar Data Acquisition subsystem to improve: – Receiver and signal processing hardware – User interface – Signal processing and diagnostic software – Reliability, maintainability and availability • ORDA improvements critical to meeting strategic goals for severe weather 3 RDA Status • Began production deployment November 2005 – Five Contractor install teams – Technical Support by RSIS Engineering and ROC – Five Installs/Week • 57 of 158 sites installed • Target completion date – September 2006 • Maintenance Training ongoing • Focus on Transition to O&M 4 5 ORDA Website http://www.orda.roc.noaa.gov/ 6 Dual Polarization 7 Acquisition Strategy Outline • Develop Performance Requirements – Leverage NSSL Experience – Include ROC and Agency Stakeholders • Continue Emphasis on Commercial Solutions • ORDA SIGMET Supports Dual Pol Modification • Performance-based acquisition – Development – Production – Deployment • Utilize NSSL work in Dual Pol Algorithm Development • Align with Current NEXRAD Maintenance Concept • Administer in OS&T/NPI 8 Dual Pol Program Activities • Issue – Contracting Officer is key member of Acquisition Team – NOAA Contracts support is constrained by limited resources – Alternative Contracting Services organization identified • FAA RESULTS Acquisition Group • Located at Mike Monroney Aeronautical Center in Oklahoma City 9 Dual Pol Program Activities (cont.) • Complete NOAA/FAA Inter-Agency Agreement with RESULTS Input • Coordinate Acquisition Plan and Inter- Agency Agreement with NOAA Acquisitions • Brief DOC Acquisition Review Board • Coordinate with RESULTS on: – Pre-Solicitation Activities – Evaluation Plan 10 Dual Pol Technical Activities • Tasking to NSSL – Hardware Analysis and Prototyping – Requirements Development – Polarimetric algorithm development (precip and HCA) – Support RFI/RFP evaluation. -
MN GIS/Lis2011conference 21St Annual Conference and Workshops
MN GIS/LIS2011Conference 21st Annual Conference and Workshops October 5–7, 2011 Saint Cloud River’s Edge Convention Center www.mngislis.org 1000 Westgate Drive, Suite 252 St. Paul, MN 55114-1067 (651) 203-7242 www.mngislis.org W LCOME Welcome to the 2011 MN GIS/LIS Conference and Workshops E The Minnesota GIS/LIS Board of Directors and Conference Committee first time. Consider attending the student competition on Thursday and welcome you to the 21st annual Minnesota GIS/LIS Conference and welcoming these future GIS professionals. Workshops. This year’s Conference will offer a unique experience to explore geographic information systems and land information systems We have an opportunity to recognize our former and current Polaris in Minnesota and beyond. and Lifetime award winners. Please take a minute to congratulate these committed individuals who are at the heart of GIS/LIS in Minnesota: MN Keynote speakers will bring us to new places, exhibiting how GIS is useful GIS/LIS honors them for their leadership. beyond our world. This year’s keynote speaker, Dr. Scott Mest, a research scientist with NASA, will introduce us to his work using GIS to study the Exhibitors support the conference and offer a variety of GIS products, surface of the moon and Mars. On Friday, we step back for a moment, services and information. The Exhibit hall is open Thursday at 10am. put down our wireless devices and unplug our laptops. Listen to Mr. Don’t forget to stop by the MN GIS/LIS Consortium booth in the exhibit Kenny Salwey, the “Last River Rat,” share his experiences living on the hall. -
2020 Infra Surface Weather Observations
Surface Weather Observations Comparison of Various Observing Systems Scott Landolt & Matthias Steiner National Center for Atmospheric Research [email protected] USHST Infrastructure Summit 12 – 13 March 2020 in Washington, DC © 2020 University Corporation for Atmospheric Research 1 Surface Stations & Reporting Frequency Station Type Frequency of Reports Automated Surface 5 minutes Observing System (ASOS) (limited access to 1 minute data) Automated Weather 20 minutes Observing System (AWOS) 15 minutes (standard), can be Road Weather Information more frequent but varies state to System (RWIS) state and even site to site 5 – 15 minutes, can vary from Mesonet station to station Iowa station network © 2020 University Corporation for Atmospheric Research 2 Reporting Variables Weather Variable ASOS AWOS RWIS Mesonet Temperature X X X X Relative X X X X Humidity/Dewpoint Wind Speed/Direction X X X X Barometric Pressure X X X X Ceiling Height X X X X Visibility X X X X Present Weather X X X X Precipitation X X X X Accumulation Road Condition X X X X X – All Stations Report X – Some Stations Report X – No Stations Report © 2020 University Corporation for Atmospheric Research 3 Station Siting Requirements Station Type Siting Areal Representativeness Automated Surface Miles (varies depending on Airport grounds, unobstructed Observing System (ASOS) local conditions & weather) Automated Weather Miles (varies depending on Airport grounds, unobstructed Observing System (AWOS) local conditions & weather) Next to roadways, can be in canyons, valleys, mountain -
Relative Forecast Impact from Aircraft, Profiler, Rawinsonde, VAD, GPS-PW, METAR and Mesonet Observations for Hourly Assimilation in the RUC
16.2 Relative forecast impact from aircraft, profiler, rawinsonde, VAD, GPS-PW, METAR and mesonet observations for hourly assimilation in the RUC Stan Benjamin, Brian D. Jamison, William R. Moninger, Barry Schwartz, and Thomas W. Schlatter NOAA Earth System Research Laboratory, Boulder, CO 1. Introduction A series of experiments was conducted using the Rapid Update Cycle (RUC) model/assimilation system in which various data sources were denied to assess the relative importance of the different data types for short-range (3h-12h duration) wind, temperature, and relative humidity forecasts at different vertical levels. This assessment of the value of 7 different observation data types (aircraft (AMDAR and TAMDAR), profiler, rawinsonde, VAD (velocity azimuth display) winds, GPS precipitable water, METAR, and mesonet) on short-range numerical forecasts was carried out for a 10-day period from November- December 2006. 2. Background Observation system experiments (OSEs) have been found very useful to determine the impact of particular observation types on operational NWP systems (e.g., Graham et al. 2000, Bouttier 2001, Zapotocny et al. 2002). This new study is unique in considering the effects of most of the currently assimilated high-frequency observing systems in a 1-h assimilation cycle. The previous observation impact experiments reported in Benjamin et al. (2004a) were primarily for wind profiler and only for effects on wind forecasts. This new impact study is much broader than that the previous study, now for more observation types, and for three forecast fields: wind, temperature, and moisture. Here, a set of observational sensitivity experiments (Table 1) were carried out for a recent winter period using 2007 versions of the Rapid Update Cycle assimilation system and forecast model. -
Weatherscope Weatherscope Application Information: Weatherscope Is a Stand-Alone Application That Makes Viewing Weather Data Easier
User Guide - Macintosh http://earthstorm.ocs.ou.edu WeatherScope WeatherScope Application Information: WeatherScope is a stand-alone application that makes viewing weather data easier. To run WeatherScope, Mac OS X version 10.3.7, a minimum of 512MB of RAM, and an accelerated graphics card with 32MB of VRAM are required. WeatherScope is distributed freely for noncommercial and educational use and can be used on both Apple Macintosh and Windows operating systems. How do I Download WeatherScope? To download the application, go to http://earthstorm.ocs.ou.edu, select Data, Software, Download, or go to http://www. ocs.ou.edu/software. There will be three options: WeatherBuddy, WeatherScope, and WxScope Plugin. You will want to choose WeatherScope. There are two options under the application: Macintosh or Windows. Choose Macintosh to download the application. The installation wizard will automatically save to your desktop. Go to your desktop and double click on the icon that says WeatherScope- x.x.x.pkg. Several dialog messages will appear. The fi rst message will inform you that you are about to install the application. The next message tells you about computer system requirements in order to download the application. The following message is the Software License Agreement. It is strongly suggested that you read this agreement. If you agree, click Agree. If you do not agree, click Disagree and the software will not be installed onto your computer. The next message asks you to select a destination drive (usually your hard drive). The setup will run and install the software on your computer. You may then press Close. -
Reading Mesonet Rain Maps
Oklahoma’s Weather Network Lesson 2 - Rain Maps Reading Mesonet Rain Maps Estimated Lesson Time: 30 minutes Introduction Every morning when you get ready for school, you decide what you are going to wear for the day. Often you might ask your parents what the weather is like or check the weather yourself before getting dressed. Then you can decide if you will wear a t-shirt or sweater, flip-flops or rain boots. The Oklahoma Mesonet, www.mesonet.org, is a weather and climate network covering the state. The Mesonet collects measurements such as air tempera- ture, rainfall, wind speed, and wind direction, every five minutes . These mea- surements are provided free to the public online. The Mesonet has 120 remote weather stations across the state collecting data. There is at least one in every county which means there is one located near you. Our data is used by people across the state. Farmers use our data to grow their crops, and firefighters use it to help put out a fire. Emergency managers in your town use it to warn you of tornadoes, and sound the town’s sirens. Mesonet rainfall data gives a statewide view, updated every five minutes. When reading the Mesonet rainfall accumulation maps, notice each Mesonet site displays accumulated rainfall. The map also displays the National Weather Service (NWS) River Forecast Center’s rainfall estimates (in color) across Oklahoma based on radar (an instrument that can locate precipitation and its motion). For example, areas in blue have lower rainfall than areas in red or purple. -
Multifunction Phased Array Radar
MULTIFUNCTION PHASED ARRAY RADAR PROGRAM STATUS AND POTENTIAL SERVICE IMPROVEMENTS Prepared by: Working Group for Multifunction Phased Array Radar (WG/MPAR) August 2009 Office of the Federal Coordinator for Meteorological Services and Supporting Research (OFCM) 8455 Colesville Road, Suite 1500 Silver Spring, MD 20910 Foreword Weather radar has proven its value to the Nation since the installation of the current weather surveillance network began in 1990. In 2020, the WSR-88D radars forming this NEXRAD network will be 23 to 30 years old. In about the same time frame, most of the Nation’s aircraft surveillance radars will be nearing the end of their design life. Decisions on replacing or repairing and upgrading these National radar assets must be made over the next 10 to 15 years. We are now on the threshold of a revolution in civilian radar capability, enabled by the adaptation of established military radar technology to existing civilian applications, plus new capabilities beyond what current systems can provide. The Working Group for Multifunction Phased Array Radar (WG/MPAR) is coordinating an interagency initiative to investigate the feasibility of applying the capabilities of phased array technology to perform weather and aircraft surveillance simultaneously. This paper presents a snapshot of the status of that initiative and projects potential service improvements that would be derived from the operational application of phased array radar to aircraft and weather surveillance. I wish to thank the members of WG/MPAR—especially Dr. James (Jeff) Kimpel, Director of the National Severe Storms Laboratory (NOAA) and Mr. William Benner, Weather Processors Group Manager (FAA)—as well as the OFCM staff, especially Mr. -
Alternative Earth Science Datasets for Identifying Patterns and Events
https://ntrs.nasa.gov/search.jsp?R=20190002267 2020-02-17T17:17:45+00:00Z Alternative Earth Science Datasets For Identifying Patterns and Events Kaylin Bugbee1, Robert Griffin1, Brian Freitag1, Jeffrey Miller1, Rahul Ramachandran2, and Jia Zhang3 (1) University of Alabama in Huntsville (2) NASA MSFC (3) Carnegie Mellon Universityv Earth Observation Big Data • Earth observation data volumes are growing exponentially • NOAA collects about 7 terabytes of data per day1 • Adds to existing 25 PB archive • Upcoming missions will generate another 5 TB per day • NASA’s Earth observation data is expected to grow to 131 TB of data per day by 20222 • NISAR and other large data volume missions3 Over the next five years, the daily ingest of data into the • Other agencies like ESA expect data EOSDIS archive is expected to grow significantly, to more 4 than 131 terabytes (TB) of forward processing. NASA volumes to continue to grow EOSDIS image. • How do we effectively explore and search through these large amounts of data? Alternative Data • Data which are extracted or generated from non-traditional sources • Social media data • Point of sale transactions • Product reviews • Logistics • Idea originates in investment world • Include alternative data sources in investment decision making process • Earth observation data is a growing Image Credit: NASA alternative data source for investing • DMSP and VIIRS nightlight data Alternative Data for Earth Science • Are there alternative data sources in the Earth sciences that can be used in a similar manner? • -
TC Modelling and Data Assimilation
Tropical Cyclone Modeling and Data Assimilation Jason Sippel NOAA AOML/HRD 2021 WMO Workshop at NHC Outline • History of TC forecast improvements in relation to model development • Ongoing developments • Future direction: A new model History: Error trends Official TC Track Forecast Errors: • Hurricane track forecasts 1990-2020 have improved markedly 300 • The average Day-3 forecast location error is 200 now about what Day-1 error was in 1990 100 • These improvements are 1990 2020 largely tied to improvements in large- scale forecasts History: Error trends • Hurricane track forecasts have improved markedly • The average Day-3 forecast location error is now about what Day-1 error was in 1990 • These improvements are largely tied to improvements in large- scale forecasts History: Error trends Official TC Intensity Forecast Errors: 1990-2020 • Hurricane intensity 30 forecasts have only recently improved 20 • Improvement in intensity 10 forecast largely corresponds with commencement of 0 1990 2020 Hurricane Forecast Improvement Project HFIP era History: Error trends HWRF Intensity Skill 40 • Significant focus of HFIP has been the 20 development of the HWRF better 0 Climo better HWRF model -20 -40 • As a result, HWRF intensity has improved Day 1 Day 3 Day 5 significantly over the past decade HWRF skill has improved up to 60%! Michael Talk focus: How better use of data, particularly from recon, has helped improve forecasts Michael Talk focus: How better use of data, particularly from recon, has helped improve forecasts History: Using TC Observations -
Noaa Technical Memorandum Nwswr-177 Climate Of
NOAA TECHNICAL MEMORANDUM NWSWR-177 CLIMATE OF PHOENIX, ARIZONA Robert J. Schmidli and Austin Jamison NEXRAD Weather Service Forecast Office Phoenix9 Arizona July 1996 Third Revision u.s. DEPARTMENT OF I National Oceanic and National Weather COMMERCE Atmospheric Administration I Service Williams, Jr., May 1972. (COM 72 10707) 76 Monthly Climatological Charts of the Behavior of Fog and Low Stratus at Los Angeles lntematicnal Airport Donald M. Gales, July 1972. (COM 7211140) n A Study of Radar Echo Distribution in Arizona During July and August John E. Hales, Jr., July 1972. (COM 7211136) 78 Forecasting Precipitation at Bakersfield, California, Using Pressure Gradient Vectors. Earl T. Riddiough, July 1972. (COM 7211146) 79 Climate of Stockton, Calffomia. Robert C. Nelson, July 1972. (COM 7210920) NOAA TECHNICAL MEMORANDA 80 Estimation of Number of Days Above or Below Selected Temperatures. Clarence M. Sakamoto, National Weather Service, Western Region Subseries October 1972. (COM 72 10021) . 81 An Aid fer Forecasting Summer Maximum Temperatures at Seattie, Washington. Edgar G. Johnson, November 1972. (COM 7310150) The National Weather Service (NWS) Westem Region (WR) Subseries provides an informal medium for the documentation and quick dissemination of results not appropriate, or not yet ready, for formal 82 Flash Flood Forecasting and Warning Program in the Western Region. Phifip Williams, Jr., ChesterLGJenn, and Roland L. Raetz, December1972, (Revised March 1978). (COM 7310251) publication. The series is used to report on work in progress, to describe technical Proc:e'dures and 83 A comparison of Manual and Semiautomatic Methods of Digitizing Analog Wind Records. Glenn practices, or to relate progress to a limited audience. -
Evaluating NEXRAD Multisensor Precipitation Estimates for Operational Hydrologic Forecasting
JUNE 2000 YOUNG ET AL. 241 Evaluating NEXRAD Multisensor Precipitation Estimates for Operational Hydrologic Forecasting C. BRYAN YOUNG,A.ALLEN BRADLEY,WITOLD F. K RAJEWSKI, AND ANTON KRUGER Iowa Institute of Hydraulic Research and Department of Civil and Environmental Engineering, The University of Iowa, Iowa City, Iowa MARK L. MORRISSEY Environmental Veri®cation and Analysis Center, University of Oklahoma, Norman, Oklahoma (Manuscript received 9 August 1999, in ®nal form 10 January 2000) ABSTRACT Next-Generation Weather Radar (NEXRAD) multisensor precipitation estimates will be used for a host of applications that include operational stream¯ow forecasting at the National Weather Service River Forecast Centers (RFCs) and nonoperational purposes such as studies of weather, climate, and hydrology. Given these expanding applications, it is important to understand the quality and error characteristics of NEXRAD multisensor products. In this paper, the issues involved in evaluating these products are examined through an assessment of a 5.5-yr record of multisensor estimates from the Arkansas±Red Basin RFC. The objectives were to examine how known radar biases manifest themselves in the multisensor product and to quantify precipitation estimation errors. Analyses included comparisons of multisensor estimates based on different processing algorithms, com- parisons with gauge observations from the Oklahoma Mesonet and the Agricultural Research Service Micronet, and the application of a validation framework to quantify error characteristics. This study reveals several com- plications to such an analysis, including a paucity of independent gauge data. These obstacles are discussed and recommendations are made to help to facilitate routine veri®cation of NEXRAD products. 1. Introduction WSR-88D radars and a network of gauges that report observations to the RFC in near±real time (Fulton et al.